A Beginner's Guide to Machine Learning
The term machine learningtext in italic sounds more like terminology straight out of a science fiction movie; nevertheless, we think machine learning technology holds better possibilities in the near future.
In simple words, Think of machine learning as an elephant. Elephants can't be explicitly programmed. You can teach them to do tricks like you might to a dog, but really the best way to get the elephant to where you want it to go is by passing it along the path that you want it to take and letting it absorb everything around them as they follow your direction.
This blog will look at the history of machine learning and how it works.
A machine that could learn would acquire information, store and recall it, and make logical decisions based on it, all without any human intervention, machine learning is the engine that drives this functionality.
Today, the term machine learning encompasses a host of different machine-learning algorithms, each with its own advantages and trade-offs.
But when we look at the inception of the term machine learning, it takes us all the way back to 1959, where Artur Samuel, a pioneer in computer gaming and artificial intelligence, catalyzed the phrase machine learning.
Arthur Samuel described machine learning as the area of study that grants computers the capability to learn and advance from their surroundings without the help of vigorous programming.
Machine Learning falls directly as the sub-category of artificial intelligence, further categorized into deep learning and neural networks. The sub-categories deal with similar yet practically distinctive knowledge and improve from the surroundings.
Machine learning is the systematic process of enabling machines to learn and make more human-like behaviors and decisions with minimal human-established programming. To enhance the computer way of thinking and improve the decision-making process, well-refined and superior data are served to the machines. Different kinds of algorithms are used to train machine learning models containing specific types of data or upon receiving certain exceptional data. The algorithm chosen depends upon the type of data, and what kind of action needs to be automated.
The algorithm-based data analysis and automation make machine learning distinctive in features compared to traditional programming, wherein the entire data and pre-programmed actions are entered into the machine to originate the expected output. On the other hand, both the input and output data are fed into the devices in the cognition phase and leave the rest of the processes like solution finding and behavior generation and self-programming to the machine itself.
Though it is often believed that the rise of machine learning will cause increased levels of unemployment as both menial labor and occupations which require little thinking, creativity or emotion can be automated for faster efficiency by having machines do them, there are several ways where humans can benefit from the rise of machine learning.
● Time efficiency ● Productiveness ● Preciseness
These might be the most prominent factors that can help businesses achieve the maximum output even when considering their daily routine tasks like data analysis and decision making. With the help of machine learning, the decision-making process within an organization can now be more simple and effective than ever, as it breaks down complex data into a simple, digestible format.
Now let's have a look at the different types of machine learning:-
Machine learning has been categorized into 4 different types [Supervised, unsupervised, semi-supervised, and reinforcement learning] based on different approaches and the challenges they can solve.
Supervised Learning
Supervised learning is a powerful approach to machine learning that uses labeled samples to develop accurate classifiers and predictors under complete supervision. Because it provides one with illustrations that show the outcome of a test. One can create predictive models based on one’s desired results. Supervised learning algorithms include statistical methods such as regression analysis, neural network modeling, and probabilistic/classification methods.
Unsupervised Learning
This approach is ingenious and innovative, where the output is unknown, but one wants to attempt to decipher the data as much as possible. While using this algorithm, one can learn a lot about the data provided. While this approach may seem intimidating, it's important to remember it's not blind testing with zero output; instead, this is a progressive method that initiates impressive data analysis and its associated work.
Semi-Supervised Learning
When it comes to semi-supervised learning, data scientists are essentially following two steps: clustering and labeling. First, the algorithms cluster similar pieces of data together and find patterns in their relationships; these patterns help guide what additional information should be labeled. The next step is to mark this data as accurately as possible with small primary details. After this process, one can more easily leverage supervised learning algorithms to proceed with the given analysis.
Reinforcement Learning
This is a reward-based approach where the main objective here is to train a machine by rewarding it for correct actions and critical feedback when making decisions. This approach is used for simulation games and robotics. Compared to supervised learning, there isn't always an answer available in reinforcement learning, so one needs an agent that decides how effective a step will be when it comes to performing a specific task. Using this method, machines can learn through personal experiences rather than just relying on a program's information like supervised learning.
Machine Learning algorithms are helping to build intelligent systems that can learn from their past experiences and historical data to give accurate results. Many businesses use this solution to either develop such a system or break down complex data into a more straightforward and understandable format and then rely on those assessments to make critical decisions. Healthcare, search engine optimization ( SEO ), defense, financial services, marketing, and security services corporations, among others, apply machine learning solutions to their business problems or to create new services or solutions.
In a nutshell,
We live in the age of data. Everything from emails and website visits, your physical location and purchasing history is being recorded, stored, and analyzed by entities you may or may not know about. That's because we live in a society where analytics have become an integral part of our daily lives due to the rapid growth this industry has seen over the past decade.
In fact, machine learning has gone mainstream, with so many people using it every day without even realizing what they're using was created by implementing Machine Learning principles. It's no wonder that more and more companies are investing heavily in machine learning solutions to help them gain new insights into their business operations & sales processes.
We hope you enjoyed our article about Machine Learning. We are optimistic that this blog has been able to give you an insight into the field of machine learning, and we genuinely hope that you are excited about the future possibilities of machine learning as we are.
Author: Aswin Viswanath